Explains the drivers and goals of data integration
Managing complexity and cost of integration, maintaining interfaces, timely shared data; goals and principles of DII.
Can AI do this?
Not settled yet The benchmark tested this, but the models' answers do not settle yet whether they master it. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 60% | Not shown |
| gemini-3.5-flash-lite | Low cost | 60% | Not shown |
| gpt-6-luna | Low cost | 60% | Not shown |
| claude-haiku-5-5 | Low cost | 60% | Not shown |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Managing complexity and cost of integration, maintaining interfaces, timely shared data; goals and principles of DII.
How it is assessed: Answers a CDMP-style multiple-choice question on the drivers of data integration
All knobits of Data integration and interoperability.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 8: Data Integration and Interoperability, element NV-7